In the last five years, there has been a boom in self-driving car simulators for perception, planning and control [1]}, [2]}, [3]}. One of the major drawbacks of such simulators is the limitation in scalability for data generation. Such simulators are shipped with a limited number of scenes (or towns). This is because it requires intensive manual labor to generate these 3D environments for better scalability and generalizability. Scalability in these simulators is present for traffic simulations but not for perception tasks. For instance, CARLA [3]} library includes only 40 different building models. Our method relies on 3D structures of buildings from satellite semantics and 3D maps to build countless numbers of building models.
